Use the Climate Data Catalog

Once we generate the catalog in the other notebook, we can use the catalog!

Imports

import intake
from distributed import Client, LocalCluster
import hvplot.xarray
import matplotlib.pyplot as plt
import holoviews as hv
hv.extension("bokeh")

Spin up a Dask Cluster

cluster = LocalCluster()
client = Client(cluster)
client

2022-05-12 19:17:34,732 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-ihnwtxn5', purging
2022-05-12 19:17:34,736 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-rpinhfj7', purging
2022-05-12 19:17:34,737 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-k9cqvfin', purging
2022-05-12 19:17:34,738 - distributed.diskutils - INFO - Found stale lock file and directory '/Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-l3mrmt5k', purging

Client

Client-16c33222-d252-11ec-9b4a-acde48001122

Connection method: Cluster object	Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

LocalCluster

9fc7b286

Dashboard: http://127.0.0.1:8787/status	Workers: 4
Total threads: 12	Total memory: 16.00 GiB
Status: running	Using processes: True

Scheduler Info

Scheduler

Scheduler-f80f6d16-1678-4b2d-bc31-05dd3123bed9

Comm: tcp://127.0.0.1:61930	Workers: 4
Dashboard: http://127.0.0.1:8787/status	Total threads: 12
Started: Just now	Total memory: 16.00 GiB

Workers

Worker: 0

Comm: tcp://127.0.0.1:61946	Total threads: 3
Dashboard: http://127.0.0.1:61950/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:61936
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-b2pjahsi

Worker: 1

Comm: tcp://127.0.0.1:61948	Total threads: 3
Dashboard: http://127.0.0.1:61955/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:61935
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-1096onay

Worker: 2

Comm: tcp://127.0.0.1:61947	Total threads: 3
Dashboard: http://127.0.0.1:61951/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:61934
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-3knsoa3c

Worker: 3

Comm: tcp://127.0.0.1:61945	Total threads: 3
Dashboard: http://127.0.0.1:61949/status	Memory: 4.00 GiB
Nanny: tcp://127.0.0.1:61933
Local directory: /Users/mgrover/git_repos/cloud-for-climate/notebooks/dask-worker-space/worker-q45o7cpx

Access the data

We have an intake catalog we can read in!

data_catalog = intake.open_catalog('catalogs/test-catalog.yml')

data_catalog["cesm-test-dataset"]

cesm-test-dataset:
  args:
    storage_options:
      fo: merged-data.json
      remote_options:
        token: anon
      remote_protocol: s3
    urlpath: reference://
  description: CESM Test Dataset
  driver: intake_xarray.xzarr.ZarrSource
  metadata:
    catalog_dir: /Users/mgrover/git_repos/cloud-for-climate/notebooks/catalogs/

Load the Data Using Dask

ds = data_catalog["cesm-test-dataset"].to_dask()
ds

<xarray.Dataset>
Dimensions:                  (time: 12, lat: 192, ilev: 71, lev: 70, lon: 288,
                              nbnd: 2, zlon: 1)
Coordinates:
  * ilev                     (ilev) float64 4.5e-06 7.42e-06 ... 985.1 1e+03
  * lat                      (lat) float64 -90.0 -89.06 -88.12 ... 89.06 90.0
  * lev                      (lev) float64 5.96e-06 9.827e-06 ... 976.3 992.6
  * lon                      (lon) float64 0.0 1.25 2.5 ... 356.2 357.5 358.8
  * time                     (time) object 2035-02-01 00:00:00 ... 2036-01-01...
  * zlon                     (zlon) float64 0.0
Dimensions without coordinates: nbnd
Data variables: (12/36)
    P0                       (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>
    ch4vmr                   (time) float64 dask.array<chunksize=(12,), meta=np.ndarray>
    co2vmr                   (time) float64 dask.array<chunksize=(12,), meta=np.ndarray>
    date                     (time) float64 dask.array<chunksize=(12,), meta=np.ndarray>
    date_written             (time) object dask.array<chunksize=(1,), meta=np.ndarray>
    datesec                  (time) float64 dask.array<chunksize=(12,), meta=np.ndarray>
    ...                       ...
    sol_tsi                  (time) float64 dask.array<chunksize=(12,), meta=np.ndarray>
    time_bnds                (time, nbnd) object dask.array<chunksize=(1, 2), meta=np.ndarray>
    time_written             (time) object dask.array<chunksize=(1,), meta=np.ndarray>
    wet_deposition_NHx_as_N  (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
    wet_deposition_NOy_as_N  (time, lat, lon) float32 dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
    zlon_bnds                (time, zlon, nbnd) float64 dask.array<chunksize=(1, 1, 2), meta=np.ndarray>
Attributes:
    Conventions:       CF-1.0
    case:              b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001
    host:               
    initial_file:      b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.001.c...
    logname:           geostrat
    model_doi_url:     https://doi.org/10.5065/D67H1H0V
    source:            CAM
    time_period_freq:  month_1
    topography_file:   /scratch/geostrat/inputdata/atm/cam/topo/fv_0.9x1.25_n...

xarray.Dataset

• Dimensions:
  • time: 12
  • lat: 192
  • ilev: 71
  • lev: 70
  • lon: 288
  • nbnd: 2
  • zlon: 1
• Coordinates: (6)
  • ilev
    (ilev)
    float64
    4.5e-06 7.42e-06 ... 985.1 1e+03

    formula_terms :

    a: hyai b: hybi p0: P0 ps: PS

    long_name :

    hybrid level at interfaces (1000*(A+B))

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    units :

    hPa

    array([4.500500e-06, 7.420100e-06, 1.223370e-05, 2.017000e-05, 3.325450e-05,
           5.482750e-05, 9.039800e-05, 1.490400e-04, 2.457200e-04, 4.051250e-04,
           6.679400e-04, 1.101265e-03, 1.815650e-03, 2.993500e-03, 4.963000e-03,
           8.150651e-03, 1.347700e-02, 2.231900e-02, 3.679650e-02, 6.066500e-02,
           9.915650e-02, 1.573900e-01, 2.388500e-01, 3.452000e-01, 4.751350e-01,
           6.318050e-01, 8.291550e-01, 1.082740e+00, 1.406850e+00, 1.818850e+00,
           2.339800e+00, 2.995050e+00, 3.814700e+00, 4.834450e+00, 6.096350e+00,
           7.649350e+00, 9.550100e+00, 1.186400e+01, 1.466550e+01, 1.803800e+01,
           2.207550e+01, 2.688250e+01, 3.257350e+01, 3.927300e+01, 4.711450e+01,
           5.624050e+01, 6.680050e+01, 8.070142e+01, 9.494104e+01, 1.116932e+02,
           1.314013e+02, 1.545868e+02, 1.818634e+02, 2.139528e+02, 2.517044e+02,
           2.961172e+02, 3.483666e+02, 4.098352e+02, 4.821499e+02, 5.672244e+02,
           6.523330e+02, 7.304459e+02, 7.963631e+02, 8.453537e+02, 8.737159e+02,
           9.003246e+02, 9.249645e+02, 9.474323e+02, 9.675386e+02, 9.851122e+02,
           1.000000e+03])

  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    long_name :

    latitude

    units :

    degrees_north

    array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
           -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
           -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
           -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
           -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
           -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
           -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
           -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
           -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
           -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
           -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
           -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
            23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
            28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
            34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
            40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
            45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
            51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
            57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
            62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
            68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
            73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
            79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
            85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])

  • lev
    (lev)
    float64
    5.96e-06 9.827e-06 ... 976.3 992.6

    formula_terms :

    a: hyam b: hybm p0: P0 ps: PS

    long_name :

    hybrid level at midpoints (1000*(A+B))

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    units :

    hPa

    array([5.960300e-06, 9.826900e-06, 1.620185e-05, 2.671225e-05, 4.404100e-05,
           7.261275e-05, 1.197190e-04, 1.973800e-04, 3.254225e-04, 5.365325e-04,
           8.846025e-04, 1.458457e-03, 2.404575e-03, 3.978250e-03, 6.556826e-03,
           1.081383e-02, 1.789800e-02, 2.955775e-02, 4.873075e-02, 7.991075e-02,
           1.282732e-01, 1.981200e-01, 2.920250e-01, 4.101675e-01, 5.534700e-01,
           7.304800e-01, 9.559475e-01, 1.244795e+00, 1.612850e+00, 2.079325e+00,
           2.667425e+00, 3.404875e+00, 4.324575e+00, 5.465400e+00, 6.872850e+00,
           8.599725e+00, 1.070705e+01, 1.326475e+01, 1.635175e+01, 2.005675e+01,
           2.447900e+01, 2.972800e+01, 3.592325e+01, 4.319375e+01, 5.167750e+01,
           6.152050e+01, 7.375096e+01, 8.782123e+01, 1.033171e+02, 1.215472e+02,
           1.429940e+02, 1.682251e+02, 1.979081e+02, 2.328286e+02, 2.739108e+02,
           3.222419e+02, 3.791009e+02, 4.459926e+02, 5.246872e+02, 6.097787e+02,
           6.913894e+02, 7.634045e+02, 8.208584e+02, 8.595348e+02, 8.870202e+02,
           9.126445e+02, 9.361984e+02, 9.574855e+02, 9.763254e+02, 9.925561e+02])

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    long_name :

    longitude

    units :

    degrees_east

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • time
    (time)
    object
    2035-02-01 00:00:00 ... 2036-01-...

    bounds :

    time_bnds

    long_name :

    time

    array([cftime.DatetimeNoLeap(2035, 2, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 3, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 4, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 5, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 6, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 7, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 8, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 9, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 10, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 11, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2035, 12, 1, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2036, 1, 1, 0, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • zlon
    (zlon)
    float64
    0.0

    bounds :

    zlon_bnds

    long_name :

    longitude

    units :

    degrees_east

    array([0.])

• Data variables: (36)
  • P0
    (time)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    reference pressure

    units :

    Pa

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • ch4vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    ch4 volume mixing ratio

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • co2vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    co2 volume mixing ratio

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • date
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current date (YYYYMMDD)

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • date_written
    (time)
    object
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type object numpy.ndarray

  • datesec
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current seconds of current date

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • f11vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    f11 volume mixing ratio

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • f12vmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    f12 volume mixing ratio

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • gw
    (time, lat)
    float64
    dask.array<chunksize=(1, 192), meta=np.ndarray>

    long_name :

    latitude weights

    Array Chunk Bytes 18.00 kiB 1.50 kiB Shape (12, 192) (1, 192) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • hyai
    (time, ilev)
    float64
    dask.array<chunksize=(1, 71), meta=np.ndarray>

    long_name :

    hybrid A coefficient at layer interfaces

    Array Chunk Bytes 6.66 kiB 568 B Shape (12, 71) (1, 71) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • hyam
    (time, lev)
    float64
    dask.array<chunksize=(1, 70), meta=np.ndarray>

    long_name :

    hybrid A coefficient at layer midpoints

    Array Chunk Bytes 6.56 kiB 560 B Shape (12, 70) (1, 70) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • hybi
    (time, ilev)
    float64
    dask.array<chunksize=(1, 71), meta=np.ndarray>

    long_name :

    hybrid B coefficient at layer interfaces

    Array Chunk Bytes 6.66 kiB 568 B Shape (12, 71) (1, 71) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • hybm
    (time, lev)
    float64
    dask.array<chunksize=(1, 70), meta=np.ndarray>

    long_name :

    hybrid B coefficient at layer midpoints

    Array Chunk Bytes 6.56 kiB 560 B Shape (12, 70) (1, 70) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • mdt
    (time)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    timestep

    units :

    s

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • n2ovmr
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    n2o volume mixing ratio

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • nbdate
    (time)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    base date (YYYYMMDD)

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • nbsec
    (time)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    seconds of base date

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • ndbase
    (time)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    base day

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • ndcur
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current day (from base day)

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • nsbase
    (time)
    float64
    dask.array<chunksize=(1,), meta=np.ndarray>

    long_name :

    seconds of base day

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

  • nscur
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current seconds of current day

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • nsteph
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    current timestep

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • soa5_a2_CHML
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 35, 96, 144), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    chemical loss rate

    mdims :

    1

    units :

    /cm3/s

    Array Chunk Bytes 177.19 MiB 1.85 MiB Shape (12, 70, 192, 288) (1, 35, 96, 144) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray

  • soa5_a2_sfgaex1
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    soa5_a2 gas-aerosol-exchange primary column tendency

    units :

    kg/m2/s

    Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 13 Tasks 12 Chunks Type float32 numpy.ndarray

  • soa5_c1
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 35, 96, 144), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    soa5_c1 in cloud water

    mdims :

    1

    units :

    kg/kg

    Array Chunk Bytes 177.19 MiB 1.85 MiB Shape (12, 70, 192, 288) (1, 35, 96, 144) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray

  • soa5_c1DDF
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    soa5_c1 dry deposition flux at bottom (grav + turb)

    units :

    kg/m2/s

    Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 13 Tasks 12 Chunks Type float32 numpy.ndarray

  • soa5_c1SFWET
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    soa5_c1 wet deposition flux at surface

    units :

    kg/m2/s

    Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 13 Tasks 12 Chunks Type float32 numpy.ndarray

  • soa5_c2
    (time, lev, lat, lon)
    float32
    dask.array<chunksize=(1, 35, 96, 144), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    soa5_c2 in cloud water

    mdims :

    1

    units :

    kg/kg

    Array Chunk Bytes 177.19 MiB 1.85 MiB Shape (12, 70, 192, 288) (1, 35, 96, 144) Count 97 Tasks 96 Chunks Type float32 numpy.ndarray

  • soa5_c2DDF
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    soa5_c2 dry deposition flux at bottom (grav + turb)

    units :

    kg/m2/s

    Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 13 Tasks 12 Chunks Type float32 numpy.ndarray

  • soa5_c2SFWET
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    soa5_c2 wet deposition flux at surface

    units :

    kg/m2/s

    Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 13 Tasks 12 Chunks Type float32 numpy.ndarray

  • sol_tsi
    (time)
    float64
    dask.array<chunksize=(12,), meta=np.ndarray>

    long_name :

    total solar irradiance

    units :

    W/m2

    Array Chunk Bytes 96 B 96 B Shape (12,) (12,) Count 2 Tasks 1 Chunks Type float64 numpy.ndarray

  • time_bnds
    (time, nbnd)
    object
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    long_name :

    time interval endpoints

    Array Chunk Bytes 192 B 16 B Shape (12, 2) (1, 2) Count 13 Tasks 12 Chunks Type object numpy.ndarray

  • time_written
    (time)
    object
    dask.array<chunksize=(1,), meta=np.ndarray>

    Array Chunk Bytes 96 B 8 B Shape (12,) (1,) Count 13 Tasks 12 Chunks Type object numpy.ndarray

  • wet_deposition_NHx_as_N
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    NHx wet deposition

    units :

    kg/m2/s

    Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 13 Tasks 12 Chunks Type float32 numpy.ndarray

  • wet_deposition_NOy_as_N
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 192, 288), meta=np.ndarray>

    cell_methods :

    time: mean

    long_name :

    NOy wet deposition

    units :

    kg/m2/s

    Array Chunk Bytes 2.53 MiB 216.00 kiB Shape (12, 192, 288) (1, 192, 288) Count 13 Tasks 12 Chunks Type float32 numpy.ndarray

  • zlon_bnds
    (time, zlon, nbnd)
    float64
    dask.array<chunksize=(1, 1, 2), meta=np.ndarray>

    long_name :

    zlon bounds

    units :

    degrees_east

    Array Chunk Bytes 192 B 16 B Shape (12, 1, 2) (1, 1, 2) Count 13 Tasks 12 Chunks Type float64 numpy.ndarray

• Attributes: (9)

  Conventions :

  CF-1.0

  case :

  b.e21.BW.f09_g17.SSP245-TSMLT-GAUSS-LOWER-0.5.001

  host :

  initial_file :

  b.e21.BWSSP245cmip6.f09_g17.CMIP6-SSP2-4.5-WACCM.001.cam.i.2035-01-01-00000.nc

  logname :

  geostrat

  model_doi_url :

  https://doi.org/10.5065/D67H1H0V

  source :

  CAM

  time_period_freq :

  month_1

  topography_file :

  /scratch/geostrat/inputdata/atm/cam/topo/fv_0.9x1.25_nc3000_Nsw042_Nrs008_Co060_Fi001_ZR_sgh30_24km_GRNL_c170103.nc

Investigate our Dataset

Let’s investigate our dataset!

Plot Using Matplotlib

We can start with a single time step

ds.wet_deposition_NHx_as_N.isel(time=0).plot();

And a single point

ds.wet_deposition_NHx_as_N.sel(lat=41.8781,
                               lon=-87.6298,
                               method='nearest').plot()
plt.title('NHx Wet Deposition near Chicago, IL')

Text(0.5, 1.0, 'NHx Wet Deposition near Chicago, IL')

Plot Using hvPlot

Let’s use an interactive plotting library!

We can start with a single time step

ds.wet_deposition_NHx_as_N.isel(time=0).hvplot(cmap='reds')

And a single point

ds.wet_deposition_NHx_as_N.sel(lat=41.8781,
                               lon=-87.6298,
                               method='nearest').hvplot.line(title='NHx Wet Deposition near Chicago, IL')

WARNING:param.CurvePlot02951: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.